Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7fb997cd01d0>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7fb997c40898>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.1
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    input_real = tf.placeholder(tf.float32, [None, image_width, image_height, image_channels], "input_real")
    input_z = tf.placeholder(tf.float32, [None, z_dim], "input_z")
    learning_rate = tf.placeholder(tf.float32, None, "learning_rate")

    return (input_real, input_z, learning_rate)


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [7]:
def discriminator(images, reuse=False, alpha=0.01):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    keep_prob = 0.5
    leaky_relu = lambda x: tf.maximum(alpha * x, x)
    
    def conv(inputs, filters, batch_norm=True):
        outputs = tf.layers.conv2d(inputs, filters, 5, 2, 'same')
        if batch_norm:
            outputs = tf.layers.batch_normalization(outputs, training=True)
        return leaky_relu(outputs)
        
    
    with tf.variable_scope("discriminator", reuse=reuse):
        # input 28*28*3
        x1 = conv(images, 64, batch_norm=False) # 14*14*64
        x2 = conv(x1, 128) # 7*7*128
        x3 = conv(x2, 256) # 4*4*256
        flat = tf.reshape(x3, (-1, 4*4*256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)

        return (out, logits)


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [8]:
def generator(z, out_channel_dim, is_train=True, alpha=0.01):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    leaky_relu = lambda x: tf.maximum(alpha * x, x)
    with tf.variable_scope("generator", reuse=not is_train):
        x1 = tf.layers.dense(z, 7*7*512)
        x1 = tf.reshape(x1, (-1, 7, 7, 512))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = leaky_relu(x1)
        # 7*7*512
        
        x2 = tf.layers.conv2d_transpose(x1, 256, 5, 1, 'SAME')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = leaky_relu(x2)
        # 7*7*256
        
        x3 = tf.layers.conv2d_transpose(x2, 128, 5, 2, 'SAME')
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = leaky_relu(x3)
        # 14*14*128
    
        logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 5, 2, 'SAME')
        out = tf.tanh(logits)
        # 28*28*out_channel_dim
        return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [9]:
def model_loss(input_real, input_z, out_channel_dim, alpha=0.9):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)

    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_logits_real) * alpha))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_logits_fake)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_logits_fake)))

    d_loss = d_loss_real + d_loss_fake

    return (d_loss, g_loss)


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [10]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    t_vars = tf.trainable_variables()
        
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]

    d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS, scope='generator')):
        g_vars = [var for var in t_vars if var.name.startswith('generator')]
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return (d_train_opt, g_train_opt)


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [11]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [12]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    input_real, input_z, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)

    d_loss, g_loss = model_loss(input_real, input_z, data_shape[3])

    d_opt, g_opt = model_opt(d_loss, g_loss, lr, beta1)
    
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            steps = 0
            for batch_images in get_batches(batch_size):
                steps +=1
                batch_images = batch_images * 2
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                # Run optimizers
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
                _ = sess.run(g_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
                
                if steps % 10 == 0:
                    train_loss_d = d_loss.eval({input_real: batch_images, input_z: batch_z})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i+1, epochs),
                          "Batch {}...".format(steps),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))

                if steps % 100 == 0:
                    show_generator_output(sess, show_n_images, input_z, data_shape[3], data_image_mode)
                
                

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [13]:
batch_size = 64
z_dim = 128
learning_rate = 0.0002
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Batch 10... Discriminator Loss: 1.0965... Generator Loss: 3.3228
Epoch 1/2... Batch 20... Discriminator Loss: 0.8951... Generator Loss: 2.4304
Epoch 1/2... Batch 30... Discriminator Loss: 0.4904... Generator Loss: 3.0226
Epoch 1/2... Batch 40... Discriminator Loss: 0.5017... Generator Loss: 2.4500
Epoch 1/2... Batch 50... Discriminator Loss: 0.5156... Generator Loss: 4.8767
Epoch 1/2... Batch 60... Discriminator Loss: 0.5434... Generator Loss: 2.2037
Epoch 1/2... Batch 70... Discriminator Loss: 0.4773... Generator Loss: 2.4682
Epoch 1/2... Batch 80... Discriminator Loss: 1.2371... Generator Loss: 0.8548
Epoch 1/2... Batch 90... Discriminator Loss: 0.7903... Generator Loss: 1.7657
Epoch 1/2... Batch 100... Discriminator Loss: 0.9895... Generator Loss: 1.0416
Epoch 1/2... Batch 110... Discriminator Loss: 0.9558... Generator Loss: 1.1234
Epoch 1/2... Batch 120... Discriminator Loss: 1.2415... Generator Loss: 0.7969
Epoch 1/2... Batch 130... Discriminator Loss: 1.1935... Generator Loss: 0.9316
Epoch 1/2... Batch 140... Discriminator Loss: 1.3015... Generator Loss: 0.6510
Epoch 1/2... Batch 150... Discriminator Loss: 1.1347... Generator Loss: 0.8019
Epoch 1/2... Batch 160... Discriminator Loss: 1.0338... Generator Loss: 1.2256
Epoch 1/2... Batch 170... Discriminator Loss: 1.0262... Generator Loss: 1.1370
Epoch 1/2... Batch 180... Discriminator Loss: 0.9547... Generator Loss: 1.0676
Epoch 1/2... Batch 190... Discriminator Loss: 1.0488... Generator Loss: 1.1634
Epoch 1/2... Batch 200... Discriminator Loss: 1.2991... Generator Loss: 0.6531
Epoch 1/2... Batch 210... Discriminator Loss: 0.9836... Generator Loss: 1.0824
Epoch 1/2... Batch 220... Discriminator Loss: 1.1525... Generator Loss: 0.6901
Epoch 1/2... Batch 230... Discriminator Loss: 1.2340... Generator Loss: 2.4834
Epoch 1/2... Batch 240... Discriminator Loss: 1.1783... Generator Loss: 1.7075
Epoch 1/2... Batch 250... Discriminator Loss: 0.9924... Generator Loss: 1.0291
Epoch 1/2... Batch 260... Discriminator Loss: 0.9835... Generator Loss: 1.6727
Epoch 1/2... Batch 270... Discriminator Loss: 0.9257... Generator Loss: 1.0545
Epoch 1/2... Batch 280... Discriminator Loss: 1.1695... Generator Loss: 0.7334
Epoch 1/2... Batch 290... Discriminator Loss: 1.0414... Generator Loss: 0.9669
Epoch 1/2... Batch 300... Discriminator Loss: 1.0061... Generator Loss: 0.9194
Epoch 1/2... Batch 310... Discriminator Loss: 1.0366... Generator Loss: 1.7009
Epoch 1/2... Batch 320... Discriminator Loss: 0.9452... Generator Loss: 1.0045
Epoch 1/2... Batch 330... Discriminator Loss: 0.9953... Generator Loss: 1.2731
Epoch 1/2... Batch 340... Discriminator Loss: 0.9948... Generator Loss: 1.2039
Epoch 1/2... Batch 350... Discriminator Loss: 0.8871... Generator Loss: 1.3174
Epoch 1/2... Batch 360... Discriminator Loss: 0.9320... Generator Loss: 1.5291
Epoch 1/2... Batch 370... Discriminator Loss: 1.2371... Generator Loss: 0.6146
Epoch 1/2... Batch 380... Discriminator Loss: 0.9589... Generator Loss: 1.7897
Epoch 1/2... Batch 390... Discriminator Loss: 1.5154... Generator Loss: 0.4426
Epoch 1/2... Batch 400... Discriminator Loss: 1.3447... Generator Loss: 0.5777
Epoch 1/2... Batch 410... Discriminator Loss: 1.0484... Generator Loss: 0.7922
Epoch 1/2... Batch 420... Discriminator Loss: 1.0781... Generator Loss: 0.8420
Epoch 1/2... Batch 430... Discriminator Loss: 1.4113... Generator Loss: 2.5148
Epoch 1/2... Batch 440... Discriminator Loss: 1.0973... Generator Loss: 0.7903
Epoch 1/2... Batch 450... Discriminator Loss: 1.2706... Generator Loss: 0.6290
Epoch 1/2... Batch 460... Discriminator Loss: 1.1018... Generator Loss: 1.7273
Epoch 1/2... Batch 470... Discriminator Loss: 1.0367... Generator Loss: 1.7850
Epoch 1/2... Batch 480... Discriminator Loss: 0.9983... Generator Loss: 1.1837
Epoch 1/2... Batch 490... Discriminator Loss: 1.1464... Generator Loss: 0.7488
Epoch 1/2... Batch 500... Discriminator Loss: 1.0571... Generator Loss: 1.3243
Epoch 1/2... Batch 510... Discriminator Loss: 0.9774... Generator Loss: 1.0672
Epoch 1/2... Batch 520... Discriminator Loss: 0.9637... Generator Loss: 1.7291
Epoch 1/2... Batch 530... Discriminator Loss: 1.2710... Generator Loss: 0.6038
Epoch 1/2... Batch 540... Discriminator Loss: 1.1030... Generator Loss: 1.9796
Epoch 1/2... Batch 550... Discriminator Loss: 1.0248... Generator Loss: 0.9971
Epoch 1/2... Batch 560... Discriminator Loss: 1.1908... Generator Loss: 0.7038
Epoch 1/2... Batch 570... Discriminator Loss: 1.0351... Generator Loss: 0.9619
Epoch 1/2... Batch 580... Discriminator Loss: 1.0489... Generator Loss: 1.1642
Epoch 1/2... Batch 590... Discriminator Loss: 1.3724... Generator Loss: 0.5511
Epoch 1/2... Batch 600... Discriminator Loss: 1.1997... Generator Loss: 0.6708
Epoch 1/2... Batch 610... Discriminator Loss: 1.0179... Generator Loss: 1.3769
Epoch 1/2... Batch 620... Discriminator Loss: 1.1051... Generator Loss: 0.7877
Epoch 1/2... Batch 630... Discriminator Loss: 0.9683... Generator Loss: 1.4673
Epoch 1/2... Batch 640... Discriminator Loss: 1.3127... Generator Loss: 0.5309
Epoch 1/2... Batch 650... Discriminator Loss: 1.1418... Generator Loss: 0.7414
Epoch 1/2... Batch 660... Discriminator Loss: 0.9577... Generator Loss: 1.6429
Epoch 1/2... Batch 670... Discriminator Loss: 0.9528... Generator Loss: 1.0952
Epoch 1/2... Batch 680... Discriminator Loss: 1.4252... Generator Loss: 0.4826
Epoch 1/2... Batch 690... Discriminator Loss: 1.0298... Generator Loss: 0.9232
Epoch 1/2... Batch 700... Discriminator Loss: 0.9224... Generator Loss: 1.3407
Epoch 1/2... Batch 710... Discriminator Loss: 1.2499... Generator Loss: 0.6068
Epoch 1/2... Batch 720... Discriminator Loss: 1.0001... Generator Loss: 1.4955
Epoch 1/2... Batch 730... Discriminator Loss: 1.0568... Generator Loss: 1.3429
Epoch 1/2... Batch 740... Discriminator Loss: 1.1912... Generator Loss: 0.7410
Epoch 1/2... Batch 750... Discriminator Loss: 1.1307... Generator Loss: 0.8616
Epoch 1/2... Batch 760... Discriminator Loss: 1.0685... Generator Loss: 0.8206
Epoch 1/2... Batch 770... Discriminator Loss: 0.9368... Generator Loss: 1.1590
Epoch 1/2... Batch 780... Discriminator Loss: 1.2147... Generator Loss: 0.7483
Epoch 1/2... Batch 790... Discriminator Loss: 1.3509... Generator Loss: 0.5387
Epoch 1/2... Batch 800... Discriminator Loss: 0.9815... Generator Loss: 1.0501
Epoch 1/2... Batch 810... Discriminator Loss: 1.1163... Generator Loss: 0.8024
Epoch 1/2... Batch 820... Discriminator Loss: 0.9965... Generator Loss: 1.7397
Epoch 1/2... Batch 830... Discriminator Loss: 0.9606... Generator Loss: 0.9552
Epoch 1/2... Batch 840... Discriminator Loss: 1.0232... Generator Loss: 1.2144
Epoch 1/2... Batch 850... Discriminator Loss: 1.4851... Generator Loss: 0.4692
Epoch 1/2... Batch 860... Discriminator Loss: 0.9603... Generator Loss: 0.9421
Epoch 1/2... Batch 870... Discriminator Loss: 1.2802... Generator Loss: 0.6094
Epoch 1/2... Batch 880... Discriminator Loss: 0.9778... Generator Loss: 0.9837
Epoch 1/2... Batch 890... Discriminator Loss: 1.0001... Generator Loss: 1.0229
Epoch 1/2... Batch 900... Discriminator Loss: 2.4909... Generator Loss: 0.1683
Epoch 1/2... Batch 910... Discriminator Loss: 1.0981... Generator Loss: 0.8049
Epoch 1/2... Batch 920... Discriminator Loss: 0.9080... Generator Loss: 1.1451
Epoch 1/2... Batch 930... Discriminator Loss: 1.0320... Generator Loss: 1.0166
Epoch 2/2... Batch 10... Discriminator Loss: 1.0632... Generator Loss: 1.0299
Epoch 2/2... Batch 20... Discriminator Loss: 1.0953... Generator Loss: 1.6363
Epoch 2/2... Batch 30... Discriminator Loss: 1.1847... Generator Loss: 0.6675
Epoch 2/2... Batch 40... Discriminator Loss: 1.3937... Generator Loss: 0.5248
Epoch 2/2... Batch 50... Discriminator Loss: 1.4903... Generator Loss: 0.4606
Epoch 2/2... Batch 60... Discriminator Loss: 0.9879... Generator Loss: 0.9493
Epoch 2/2... Batch 70... Discriminator Loss: 1.2955... Generator Loss: 0.5969
Epoch 2/2... Batch 80... Discriminator Loss: 0.9789... Generator Loss: 1.0599
Epoch 2/2... Batch 90... Discriminator Loss: 0.8910... Generator Loss: 1.2769
Epoch 2/2... Batch 100... Discriminator Loss: 0.9064... Generator Loss: 1.2976
Epoch 2/2... Batch 110... Discriminator Loss: 1.0442... Generator Loss: 1.9142
Epoch 2/2... Batch 120... Discriminator Loss: 0.9524... Generator Loss: 1.2918
Epoch 2/2... Batch 130... Discriminator Loss: 1.1502... Generator Loss: 0.8069
Epoch 2/2... Batch 140... Discriminator Loss: 1.1525... Generator Loss: 0.7491
Epoch 2/2... Batch 150... Discriminator Loss: 1.1316... Generator Loss: 0.6954
Epoch 2/2... Batch 160... Discriminator Loss: 1.2131... Generator Loss: 1.1050
Epoch 2/2... Batch 170... Discriminator Loss: 1.1201... Generator Loss: 0.7699
Epoch 2/2... Batch 180... Discriminator Loss: 1.1319... Generator Loss: 0.7425
Epoch 2/2... Batch 190... Discriminator Loss: 0.9442... Generator Loss: 0.9876
Epoch 2/2... Batch 200... Discriminator Loss: 0.9729... Generator Loss: 1.0310
Epoch 2/2... Batch 210... Discriminator Loss: 1.5015... Generator Loss: 0.4736
Epoch 2/2... Batch 220... Discriminator Loss: 1.0470... Generator Loss: 0.8036
Epoch 2/2... Batch 230... Discriminator Loss: 1.4245... Generator Loss: 0.4865
Epoch 2/2... Batch 240... Discriminator Loss: 0.8616... Generator Loss: 2.0324
Epoch 2/2... Batch 250... Discriminator Loss: 0.9914... Generator Loss: 1.2172
Epoch 2/2... Batch 260... Discriminator Loss: 1.0627... Generator Loss: 0.7868
Epoch 2/2... Batch 270... Discriminator Loss: 0.8422... Generator Loss: 1.2468
Epoch 2/2... Batch 280... Discriminator Loss: 0.9487... Generator Loss: 1.0792
Epoch 2/2... Batch 290... Discriminator Loss: 0.8721... Generator Loss: 1.4120
Epoch 2/2... Batch 300... Discriminator Loss: 1.1567... Generator Loss: 0.7274
Epoch 2/2... Batch 310... Discriminator Loss: 1.1765... Generator Loss: 1.0143
Epoch 2/2... Batch 320... Discriminator Loss: 0.8597... Generator Loss: 1.2382
Epoch 2/2... Batch 330... Discriminator Loss: 0.8791... Generator Loss: 1.1466
Epoch 2/2... Batch 340... Discriminator Loss: 1.1571... Generator Loss: 0.6890
Epoch 2/2... Batch 350... Discriminator Loss: 0.8873... Generator Loss: 1.2003
Epoch 2/2... Batch 360... Discriminator Loss: 1.7090... Generator Loss: 0.3645
Epoch 2/2... Batch 370... Discriminator Loss: 0.8984... Generator Loss: 1.2870
Epoch 2/2... Batch 380... Discriminator Loss: 1.5007... Generator Loss: 2.9108
Epoch 2/2... Batch 390... Discriminator Loss: 0.8987... Generator Loss: 1.3115
Epoch 2/2... Batch 400... Discriminator Loss: 0.9900... Generator Loss: 1.0353
Epoch 2/2... Batch 410... Discriminator Loss: 0.9598... Generator Loss: 0.9300
Epoch 2/2... Batch 420... Discriminator Loss: 0.9252... Generator Loss: 1.0965
Epoch 2/2... Batch 430... Discriminator Loss: 0.9959... Generator Loss: 2.1644
Epoch 2/2... Batch 440... Discriminator Loss: 1.1937... Generator Loss: 0.8515
Epoch 2/2... Batch 450... Discriminator Loss: 0.8818... Generator Loss: 1.1878
Epoch 2/2... Batch 460... Discriminator Loss: 0.9681... Generator Loss: 1.1284
Epoch 2/2... Batch 470... Discriminator Loss: 0.9262... Generator Loss: 0.9459
Epoch 2/2... Batch 480... Discriminator Loss: 1.0407... Generator Loss: 0.8563
Epoch 2/2... Batch 490... Discriminator Loss: 1.2352... Generator Loss: 0.6501
Epoch 2/2... Batch 500... Discriminator Loss: 1.1934... Generator Loss: 0.6649
Epoch 2/2... Batch 510... Discriminator Loss: 2.4862... Generator Loss: 3.7143
Epoch 2/2... Batch 520... Discriminator Loss: 0.9267... Generator Loss: 1.1786
Epoch 2/2... Batch 530... Discriminator Loss: 1.0367... Generator Loss: 0.8347
Epoch 2/2... Batch 540... Discriminator Loss: 1.0012... Generator Loss: 0.8747
Epoch 2/2... Batch 550... Discriminator Loss: 0.8857... Generator Loss: 1.1136
Epoch 2/2... Batch 560... Discriminator Loss: 1.2947... Generator Loss: 0.5819
Epoch 2/2... Batch 570... Discriminator Loss: 0.8212... Generator Loss: 1.4901
Epoch 2/2... Batch 580... Discriminator Loss: 0.8183... Generator Loss: 1.4902
Epoch 2/2... Batch 590... Discriminator Loss: 1.1204... Generator Loss: 0.7149
Epoch 2/2... Batch 600... Discriminator Loss: 0.8876... Generator Loss: 1.0954
Epoch 2/2... Batch 610... Discriminator Loss: 0.8454... Generator Loss: 1.3301
Epoch 2/2... Batch 620... Discriminator Loss: 0.9559... Generator Loss: 1.4092
Epoch 2/2... Batch 630... Discriminator Loss: 0.9191... Generator Loss: 1.0185
Epoch 2/2... Batch 640... Discriminator Loss: 0.9451... Generator Loss: 1.0620
Epoch 2/2... Batch 650... Discriminator Loss: 1.4532... Generator Loss: 0.4525
Epoch 2/2... Batch 660... Discriminator Loss: 0.9896... Generator Loss: 0.9411
Epoch 2/2... Batch 670... Discriminator Loss: 0.9382... Generator Loss: 1.2220
Epoch 2/2... Batch 680... Discriminator Loss: 1.2272... Generator Loss: 0.6589
Epoch 2/2... Batch 690... Discriminator Loss: 1.0060... Generator Loss: 0.9058
Epoch 2/2... Batch 700... Discriminator Loss: 0.8879... Generator Loss: 1.2392
Epoch 2/2... Batch 710... Discriminator Loss: 0.9068... Generator Loss: 1.1120
Epoch 2/2... Batch 720... Discriminator Loss: 0.9196... Generator Loss: 1.0397
Epoch 2/2... Batch 730... Discriminator Loss: 1.0066... Generator Loss: 0.8907
Epoch 2/2... Batch 740... Discriminator Loss: 1.1829... Generator Loss: 0.6923
Epoch 2/2... Batch 750... Discriminator Loss: 0.8624... Generator Loss: 1.1426
Epoch 2/2... Batch 760... Discriminator Loss: 1.0521... Generator Loss: 0.7793
Epoch 2/2... Batch 770... Discriminator Loss: 0.8932... Generator Loss: 1.0223
Epoch 2/2... Batch 780... Discriminator Loss: 1.2747... Generator Loss: 0.6297
Epoch 2/2... Batch 790... Discriminator Loss: 0.8004... Generator Loss: 1.3661
Epoch 2/2... Batch 800... Discriminator Loss: 1.4674... Generator Loss: 0.4914
Epoch 2/2... Batch 810... Discriminator Loss: 0.8523... Generator Loss: 1.2089
Epoch 2/2... Batch 820... Discriminator Loss: 0.7879... Generator Loss: 1.2846
Epoch 2/2... Batch 830... Discriminator Loss: 0.9700... Generator Loss: 1.3598
Epoch 2/2... Batch 840... Discriminator Loss: 0.8610... Generator Loss: 1.0982
Epoch 2/2... Batch 850... Discriminator Loss: 1.5012... Generator Loss: 0.4807
Epoch 2/2... Batch 860... Discriminator Loss: 1.0844... Generator Loss: 0.7909
Epoch 2/2... Batch 870... Discriminator Loss: 1.2758... Generator Loss: 0.6157
Epoch 2/2... Batch 880... Discriminator Loss: 1.3955... Generator Loss: 0.5687
Epoch 2/2... Batch 890... Discriminator Loss: 0.9190... Generator Loss: 1.0811
Epoch 2/2... Batch 900... Discriminator Loss: 0.7564... Generator Loss: 1.5116
Epoch 2/2... Batch 910... Discriminator Loss: 1.0576... Generator Loss: 0.8232
Epoch 2/2... Batch 920... Discriminator Loss: 0.8469... Generator Loss: 1.4249
Epoch 2/2... Batch 930... Discriminator Loss: 1.0276... Generator Loss: 0.8722

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [15]:
batch_size = 32
z_dim = 100
learning_rate = 0.0002
beta1 = 0.45

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Batch 10... Discriminator Loss: 2.0700... Generator Loss: 0.2832
Epoch 1/1... Batch 20... Discriminator Loss: 0.6891... Generator Loss: 2.0900
Epoch 1/1... Batch 30... Discriminator Loss: 0.8048... Generator Loss: 1.1998
Epoch 1/1... Batch 40... Discriminator Loss: 3.8746... Generator Loss: 0.0383
Epoch 1/1... Batch 50... Discriminator Loss: 0.9927... Generator Loss: 1.1292
Epoch 1/1... Batch 60... Discriminator Loss: 0.4187... Generator Loss: 5.5632
Epoch 1/1... Batch 70... Discriminator Loss: 0.9827... Generator Loss: 1.5947
Epoch 1/1... Batch 80... Discriminator Loss: 1.2380... Generator Loss: 1.2192
Epoch 1/1... Batch 90... Discriminator Loss: 1.2455... Generator Loss: 1.0594
Epoch 1/1... Batch 100... Discriminator Loss: 1.1291... Generator Loss: 0.8414
Epoch 1/1... Batch 110... Discriminator Loss: 1.3653... Generator Loss: 0.6514
Epoch 1/1... Batch 120... Discriminator Loss: 1.1158... Generator Loss: 1.2566
Epoch 1/1... Batch 130... Discriminator Loss: 1.2336... Generator Loss: 0.8738
Epoch 1/1... Batch 140... Discriminator Loss: 1.6986... Generator Loss: 0.4260
Epoch 1/1... Batch 150... Discriminator Loss: 0.9596... Generator Loss: 1.1483
Epoch 1/1... Batch 160... Discriminator Loss: 1.1531... Generator Loss: 1.2787
Epoch 1/1... Batch 170... Discriminator Loss: 1.6914... Generator Loss: 0.5103
Epoch 1/1... Batch 180... Discriminator Loss: 1.4902... Generator Loss: 0.5469
Epoch 1/1... Batch 190... Discriminator Loss: 1.4622... Generator Loss: 0.7679
Epoch 1/1... Batch 200... Discriminator Loss: 1.2159... Generator Loss: 0.7617
Epoch 1/1... Batch 210... Discriminator Loss: 1.1510... Generator Loss: 0.9482
Epoch 1/1... Batch 220... Discriminator Loss: 1.1637... Generator Loss: 1.7305
Epoch 1/1... Batch 230... Discriminator Loss: 1.1523... Generator Loss: 0.9548
Epoch 1/1... Batch 240... Discriminator Loss: 1.0667... Generator Loss: 1.2066
Epoch 1/1... Batch 250... Discriminator Loss: 1.1848... Generator Loss: 0.7848
Epoch 1/1... Batch 260... Discriminator Loss: 1.1284... Generator Loss: 0.8232
Epoch 1/1... Batch 270... Discriminator Loss: 1.0157... Generator Loss: 1.3629
Epoch 1/1... Batch 280... Discriminator Loss: 0.9817... Generator Loss: 0.8841
Epoch 1/1... Batch 290... Discriminator Loss: 1.3687... Generator Loss: 0.5975
Epoch 1/1... Batch 300... Discriminator Loss: 1.4621... Generator Loss: 0.5630
Epoch 1/1... Batch 310... Discriminator Loss: 1.2907... Generator Loss: 0.8081
Epoch 1/1... Batch 320... Discriminator Loss: 0.9930... Generator Loss: 1.5669
Epoch 1/1... Batch 330... Discriminator Loss: 1.4030... Generator Loss: 0.5139
Epoch 1/1... Batch 340... Discriminator Loss: 1.9823... Generator Loss: 0.2927
Epoch 1/1... Batch 350... Discriminator Loss: 1.0683... Generator Loss: 0.9430
Epoch 1/1... Batch 360... Discriminator Loss: 0.9050... Generator Loss: 1.3225
Epoch 1/1... Batch 370... Discriminator Loss: 1.1466... Generator Loss: 0.8520
Epoch 1/1... Batch 380... Discriminator Loss: 1.9873... Generator Loss: 1.3132
Epoch 1/1... Batch 390... Discriminator Loss: 1.2193... Generator Loss: 1.0239
Epoch 1/1... Batch 400... Discriminator Loss: 1.3944... Generator Loss: 0.5527
Epoch 1/1... Batch 410... Discriminator Loss: 1.1140... Generator Loss: 1.4391
Epoch 1/1... Batch 420... Discriminator Loss: 1.0701... Generator Loss: 0.8754
Epoch 1/1... Batch 430... Discriminator Loss: 1.1390... Generator Loss: 0.8444
Epoch 1/1... Batch 440... Discriminator Loss: 1.0042... Generator Loss: 0.9009
Epoch 1/1... Batch 450... Discriminator Loss: 0.9867... Generator Loss: 1.0727
Epoch 1/1... Batch 460... Discriminator Loss: 1.4236... Generator Loss: 0.5598
Epoch 1/1... Batch 470... Discriminator Loss: 0.8342... Generator Loss: 1.5796
Epoch 1/1... Batch 480... Discriminator Loss: 1.2246... Generator Loss: 1.1052
Epoch 1/1... Batch 490... Discriminator Loss: 0.8731... Generator Loss: 1.1217
Epoch 1/1... Batch 500... Discriminator Loss: 1.0593... Generator Loss: 0.9989
Epoch 1/1... Batch 510... Discriminator Loss: 1.2883... Generator Loss: 2.4218
Epoch 1/1... Batch 520... Discriminator Loss: 0.8985... Generator Loss: 1.6981
Epoch 1/1... Batch 530... Discriminator Loss: 0.9757... Generator Loss: 1.6559
Epoch 1/1... Batch 540... Discriminator Loss: 1.2642... Generator Loss: 0.5866
Epoch 1/1... Batch 550... Discriminator Loss: 0.8918... Generator Loss: 1.0451
Epoch 1/1... Batch 560... Discriminator Loss: 0.8273... Generator Loss: 1.5407
Epoch 1/1... Batch 570... Discriminator Loss: 1.2926... Generator Loss: 0.6159
Epoch 1/1... Batch 580... Discriminator Loss: 1.4636... Generator Loss: 0.4624
Epoch 1/1... Batch 590... Discriminator Loss: 3.0313... Generator Loss: 0.0898
Epoch 1/1... Batch 600... Discriminator Loss: 1.0691... Generator Loss: 0.8827
Epoch 1/1... Batch 610... Discriminator Loss: 1.2570... Generator Loss: 2.4609
Epoch 1/1... Batch 620... Discriminator Loss: 1.6884... Generator Loss: 0.3753
Epoch 1/1... Batch 630... Discriminator Loss: 0.9699... Generator Loss: 0.9799
Epoch 1/1... Batch 640... Discriminator Loss: 1.3880... Generator Loss: 2.8898
Epoch 1/1... Batch 650... Discriminator Loss: 1.3453... Generator Loss: 0.6332
Epoch 1/1... Batch 660... Discriminator Loss: 1.2199... Generator Loss: 2.4775
Epoch 1/1... Batch 670... Discriminator Loss: 2.0776... Generator Loss: 0.2214
Epoch 1/1... Batch 680... Discriminator Loss: 0.8702... Generator Loss: 1.5188
Epoch 1/1... Batch 690... Discriminator Loss: 1.0587... Generator Loss: 0.9475
Epoch 1/1... Batch 700... Discriminator Loss: 0.9687... Generator Loss: 3.2829
Epoch 1/1... Batch 710... Discriminator Loss: 1.5274... Generator Loss: 3.9452
Epoch 1/1... Batch 720... Discriminator Loss: 0.7113... Generator Loss: 1.8337
Epoch 1/1... Batch 730... Discriminator Loss: 0.8471... Generator Loss: 2.7309
Epoch 1/1... Batch 740... Discriminator Loss: 1.8136... Generator Loss: 0.3111
Epoch 1/1... Batch 750... Discriminator Loss: 0.6734... Generator Loss: 1.6002
Epoch 1/1... Batch 760... Discriminator Loss: 1.2000... Generator Loss: 0.6248
Epoch 1/1... Batch 770... Discriminator Loss: 0.5746... Generator Loss: 1.9396
Epoch 1/1... Batch 780... Discriminator Loss: 0.8179... Generator Loss: 3.2294
Epoch 1/1... Batch 790... Discriminator Loss: 1.6648... Generator Loss: 4.4674
Epoch 1/1... Batch 800... Discriminator Loss: 1.5285... Generator Loss: 0.4402
Epoch 1/1... Batch 810... Discriminator Loss: 0.9975... Generator Loss: 1.2921
Epoch 1/1... Batch 820... Discriminator Loss: 0.7241... Generator Loss: 2.3190
Epoch 1/1... Batch 830... Discriminator Loss: 0.7092... Generator Loss: 1.3780
Epoch 1/1... Batch 840... Discriminator Loss: 1.3747... Generator Loss: 0.6415
Epoch 1/1... Batch 850... Discriminator Loss: 1.2770... Generator Loss: 0.6074
Epoch 1/1... Batch 860... Discriminator Loss: 1.5965... Generator Loss: 2.4691
Epoch 1/1... Batch 870... Discriminator Loss: 0.7832... Generator Loss: 2.2454
Epoch 1/1... Batch 880... Discriminator Loss: 1.3448... Generator Loss: 0.6620
Epoch 1/1... Batch 890... Discriminator Loss: 1.4152... Generator Loss: 3.1941
Epoch 1/1... Batch 900... Discriminator Loss: 0.6787... Generator Loss: 1.5868
Epoch 1/1... Batch 910... Discriminator Loss: 0.6136... Generator Loss: 3.8736
Epoch 1/1... Batch 920... Discriminator Loss: 0.9706... Generator Loss: 2.5138
Epoch 1/1... Batch 930... Discriminator Loss: 1.8162... Generator Loss: 3.6135
Epoch 1/1... Batch 940... Discriminator Loss: 1.0180... Generator Loss: 0.8224
Epoch 1/1... Batch 950... Discriminator Loss: 1.4035... Generator Loss: 0.5616
Epoch 1/1... Batch 960... Discriminator Loss: 0.8874... Generator Loss: 0.9721
Epoch 1/1... Batch 970... Discriminator Loss: 0.7086... Generator Loss: 2.3634
Epoch 1/1... Batch 980... Discriminator Loss: 0.7441... Generator Loss: 1.2346
Epoch 1/1... Batch 990... Discriminator Loss: 1.5983... Generator Loss: 0.3895
Epoch 1/1... Batch 1000... Discriminator Loss: 0.7033... Generator Loss: 1.4767
Epoch 1/1... Batch 1010... Discriminator Loss: 1.4357... Generator Loss: 0.4899
Epoch 1/1... Batch 1020... Discriminator Loss: 1.9327... Generator Loss: 0.2679
Epoch 1/1... Batch 1030... Discriminator Loss: 0.8830... Generator Loss: 1.3397
Epoch 1/1... Batch 1040... Discriminator Loss: 1.0996... Generator Loss: 0.8031
Epoch 1/1... Batch 1050... Discriminator Loss: 1.6188... Generator Loss: 2.0291
Epoch 1/1... Batch 1060... Discriminator Loss: 1.5703... Generator Loss: 0.4982
Epoch 1/1... Batch 1070... Discriminator Loss: 0.7999... Generator Loss: 1.1812
Epoch 1/1... Batch 1080... Discriminator Loss: 0.6518... Generator Loss: 1.7577
Epoch 1/1... Batch 1090... Discriminator Loss: 0.5109... Generator Loss: 2.6070
Epoch 1/1... Batch 1100... Discriminator Loss: 1.0062... Generator Loss: 3.1528
Epoch 1/1... Batch 1110... Discriminator Loss: 0.7292... Generator Loss: 4.4054
Epoch 1/1... Batch 1120... Discriminator Loss: 1.4691... Generator Loss: 0.6844
Epoch 1/1... Batch 1130... Discriminator Loss: 1.1260... Generator Loss: 0.7746
Epoch 1/1... Batch 1140... Discriminator Loss: 0.9255... Generator Loss: 1.3451
Epoch 1/1... Batch 1150... Discriminator Loss: 0.8476... Generator Loss: 1.1669
Epoch 1/1... Batch 1160... Discriminator Loss: 0.7477... Generator Loss: 1.5553
Epoch 1/1... Batch 1170... Discriminator Loss: 0.6661... Generator Loss: 2.3487
Epoch 1/1... Batch 1180... Discriminator Loss: 0.9931... Generator Loss: 1.0362
Epoch 1/1... Batch 1190... Discriminator Loss: 0.7435... Generator Loss: 1.5815
Epoch 1/1... Batch 1200... Discriminator Loss: 0.9557... Generator Loss: 1.4165
Epoch 1/1... Batch 1210... Discriminator Loss: 0.8698... Generator Loss: 2.3752
Epoch 1/1... Batch 1220... Discriminator Loss: 1.4289... Generator Loss: 0.5193
Epoch 1/1... Batch 1230... Discriminator Loss: 0.6822... Generator Loss: 1.4256
Epoch 1/1... Batch 1240... Discriminator Loss: 0.7958... Generator Loss: 1.7077
Epoch 1/1... Batch 1250... Discriminator Loss: 1.2055... Generator Loss: 0.7053
Epoch 1/1... Batch 1260... Discriminator Loss: 0.8819... Generator Loss: 1.0644
Epoch 1/1... Batch 1270... Discriminator Loss: 1.0976... Generator Loss: 1.1645
Epoch 1/1... Batch 1280... Discriminator Loss: 0.9383... Generator Loss: 0.9240
Epoch 1/1... Batch 1290... Discriminator Loss: 1.1477... Generator Loss: 0.6671
Epoch 1/1... Batch 1300... Discriminator Loss: 1.0029... Generator Loss: 1.5524
Epoch 1/1... Batch 1310... Discriminator Loss: 1.1277... Generator Loss: 0.7125
Epoch 1/1... Batch 1320... Discriminator Loss: 1.3895... Generator Loss: 0.4903
Epoch 1/1... Batch 1330... Discriminator Loss: 1.2675... Generator Loss: 0.6394
Epoch 1/1... Batch 1340... Discriminator Loss: 1.3529... Generator Loss: 0.6484
Epoch 1/1... Batch 1350... Discriminator Loss: 0.8638... Generator Loss: 1.7054
Epoch 1/1... Batch 1360... Discriminator Loss: 1.3710... Generator Loss: 0.5207
Epoch 1/1... Batch 1370... Discriminator Loss: 0.9908... Generator Loss: 1.0691
Epoch 1/1... Batch 1380... Discriminator Loss: 1.0307... Generator Loss: 0.7977
Epoch 1/1... Batch 1390... Discriminator Loss: 0.8115... Generator Loss: 1.4915
Epoch 1/1... Batch 1400... Discriminator Loss: 0.5409... Generator Loss: 2.3606
Epoch 1/1... Batch 1410... Discriminator Loss: 2.0507... Generator Loss: 0.2464
Epoch 1/1... Batch 1420... Discriminator Loss: 1.0211... Generator Loss: 0.9965
Epoch 1/1... Batch 1430... Discriminator Loss: 0.7287... Generator Loss: 1.7917
Epoch 1/1... Batch 1440... Discriminator Loss: 0.6827... Generator Loss: 1.4120
Epoch 1/1... Batch 1450... Discriminator Loss: 0.5475... Generator Loss: 2.0348
Epoch 1/1... Batch 1460... Discriminator Loss: 0.6153... Generator Loss: 1.7280
Epoch 1/1... Batch 1470... Discriminator Loss: 1.2422... Generator Loss: 0.7805
Epoch 1/1... Batch 1480... Discriminator Loss: 1.0430... Generator Loss: 1.0327
Epoch 1/1... Batch 1490... Discriminator Loss: 0.9928... Generator Loss: 0.8811
Epoch 1/1... Batch 1500... Discriminator Loss: 0.7157... Generator Loss: 1.6055
Epoch 1/1... Batch 1510... Discriminator Loss: 0.5086... Generator Loss: 2.8078
Epoch 1/1... Batch 1520... Discriminator Loss: 1.0322... Generator Loss: 0.8387
Epoch 1/1... Batch 1530... Discriminator Loss: 0.9995... Generator Loss: 0.8684
Epoch 1/1... Batch 1540... Discriminator Loss: 1.8154... Generator Loss: 1.4762
Epoch 1/1... Batch 1550... Discriminator Loss: 1.1490... Generator Loss: 1.7700
Epoch 1/1... Batch 1560... Discriminator Loss: 1.0202... Generator Loss: 0.8176
Epoch 1/1... Batch 1570... Discriminator Loss: 1.0117... Generator Loss: 1.1509
Epoch 1/1... Batch 1580... Discriminator Loss: 0.7174... Generator Loss: 1.3864
Epoch 1/1... Batch 1590... Discriminator Loss: 1.2316... Generator Loss: 0.6861
Epoch 1/1... Batch 1600... Discriminator Loss: 1.1626... Generator Loss: 0.6775
Epoch 1/1... Batch 1610... Discriminator Loss: 0.9035... Generator Loss: 0.9811
Epoch 1/1... Batch 1620... Discriminator Loss: 1.0413... Generator Loss: 0.8677
Epoch 1/1... Batch 1630... Discriminator Loss: 1.4614... Generator Loss: 0.4839
Epoch 1/1... Batch 1640... Discriminator Loss: 1.1784... Generator Loss: 0.7725
Epoch 1/1... Batch 1650... Discriminator Loss: 0.5745... Generator Loss: 2.0955
Epoch 1/1... Batch 1660... Discriminator Loss: 0.9591... Generator Loss: 1.5434
Epoch 1/1... Batch 1670... Discriminator Loss: 0.6320... Generator Loss: 1.5639
Epoch 1/1... Batch 1680... Discriminator Loss: 1.2063... Generator Loss: 1.1177
Epoch 1/1... Batch 1690... Discriminator Loss: 0.6983... Generator Loss: 2.3935
Epoch 1/1... Batch 1700... Discriminator Loss: 1.0562... Generator Loss: 1.8874
Epoch 1/1... Batch 1710... Discriminator Loss: 1.1638... Generator Loss: 0.7221
Epoch 1/1... Batch 1720... Discriminator Loss: 0.7282... Generator Loss: 1.2925
Epoch 1/1... Batch 1730... Discriminator Loss: 1.3367... Generator Loss: 0.5808
Epoch 1/1... Batch 1740... Discriminator Loss: 0.7522... Generator Loss: 1.2193
Epoch 1/1... Batch 1750... Discriminator Loss: 1.6835... Generator Loss: 0.3643
Epoch 1/1... Batch 1760... Discriminator Loss: 1.3780... Generator Loss: 1.4023
Epoch 1/1... Batch 1770... Discriminator Loss: 1.2170... Generator Loss: 0.7959
Epoch 1/1... Batch 1780... Discriminator Loss: 1.0220... Generator Loss: 0.8551
Epoch 1/1... Batch 1790... Discriminator Loss: 0.9948... Generator Loss: 1.0677
Epoch 1/1... Batch 1800... Discriminator Loss: 1.1198... Generator Loss: 0.8672
Epoch 1/1... Batch 1810... Discriminator Loss: 1.3023... Generator Loss: 0.6943
Epoch 1/1... Batch 1820... Discriminator Loss: 0.7960... Generator Loss: 1.2989
Epoch 1/1... Batch 1830... Discriminator Loss: 0.8814... Generator Loss: 1.1989
Epoch 1/1... Batch 1840... Discriminator Loss: 1.0474... Generator Loss: 0.7975
Epoch 1/1... Batch 1850... Discriminator Loss: 0.8366... Generator Loss: 1.1486
Epoch 1/1... Batch 1860... Discriminator Loss: 1.8308... Generator Loss: 0.3110
Epoch 1/1... Batch 1870... Discriminator Loss: 0.5331... Generator Loss: 2.5514
Epoch 1/1... Batch 1880... Discriminator Loss: 1.3634... Generator Loss: 0.8090
Epoch 1/1... Batch 1890... Discriminator Loss: 1.1229... Generator Loss: 0.7180
Epoch 1/1... Batch 1900... Discriminator Loss: 1.0494... Generator Loss: 0.8599
Epoch 1/1... Batch 1910... Discriminator Loss: 1.3140... Generator Loss: 0.6425
Epoch 1/1... Batch 1920... Discriminator Loss: 0.7722... Generator Loss: 1.1467
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Epoch 1/1... Batch 2200... Discriminator Loss: 0.8933... Generator Loss: 1.1087
Epoch 1/1... Batch 2210... Discriminator Loss: 0.7524... Generator Loss: 1.6198
Epoch 1/1... Batch 2220... Discriminator Loss: 0.8509... Generator Loss: 2.0564
Epoch 1/1... Batch 2230... Discriminator Loss: 0.8564... Generator Loss: 1.2457
Epoch 1/1... Batch 2240... Discriminator Loss: 0.6345... Generator Loss: 1.4731
Epoch 1/1... Batch 2250... Discriminator Loss: 0.8362... Generator Loss: 1.1150
Epoch 1/1... Batch 2260... Discriminator Loss: 0.9417... Generator Loss: 1.1618
In [14]:
batch_size = 32
z_dim = 100
learning_rate = 0.0002
beta1 = 0.45

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/2... Batch 10... Discriminator Loss: 3.0525... Generator Loss: 0.0931
Epoch 1/2... Batch 20... Discriminator Loss: 2.7828... Generator Loss: 0.1135
Epoch 1/2... Batch 30... Discriminator Loss: 1.4982... Generator Loss: 0.4549
Epoch 1/2... Batch 40... Discriminator Loss: 1.7046... Generator Loss: 7.3030
Epoch 1/2... Batch 50... Discriminator Loss: 0.7673... Generator Loss: 1.2613
Epoch 1/2... Batch 60... Discriminator Loss: 0.5173... Generator Loss: 2.9281
Epoch 1/2... Batch 70... Discriminator Loss: 0.7494... Generator Loss: 2.3607
Epoch 1/2... Batch 80... Discriminator Loss: 0.8086... Generator Loss: 1.6916
Epoch 1/2... Batch 90... Discriminator Loss: 0.8930... Generator Loss: 1.5637
Epoch 1/2... Batch 100... Discriminator Loss: 1.7868... Generator Loss: 0.3554
Epoch 1/2... Batch 110... Discriminator Loss: 0.9250... Generator Loss: 1.2438
Epoch 1/2... Batch 120... Discriminator Loss: 0.9847... Generator Loss: 1.0063
Epoch 1/2... Batch 130... Discriminator Loss: 0.9555... Generator Loss: 1.2671
Epoch 1/2... Batch 140... Discriminator Loss: 1.8156... Generator Loss: 0.5128
Epoch 1/2... Batch 150... Discriminator Loss: 1.3830... Generator Loss: 0.5881
Epoch 1/2... Batch 160... Discriminator Loss: 1.0387... Generator Loss: 1.3128
Epoch 1/2... Batch 170... Discriminator Loss: 2.2031... Generator Loss: 0.2435
Epoch 1/2... Batch 180... Discriminator Loss: 1.2126... Generator Loss: 0.8652
Epoch 1/2... Batch 190... Discriminator Loss: 1.2576... Generator Loss: 0.7260
Epoch 1/2... Batch 200... Discriminator Loss: 1.0771... Generator Loss: 1.3449
Epoch 1/2... Batch 210... Discriminator Loss: 1.0503... Generator Loss: 1.0618
Epoch 1/2... Batch 220... Discriminator Loss: 1.2353... Generator Loss: 0.8414
Epoch 1/2... Batch 230... Discriminator Loss: 1.1422... Generator Loss: 0.8747
Epoch 1/2... Batch 240... Discriminator Loss: 1.5482... Generator Loss: 0.5248
Epoch 1/2... Batch 250... Discriminator Loss: 1.3176... Generator Loss: 0.6996
Epoch 1/2... Batch 260... Discriminator Loss: 1.2964... Generator Loss: 1.3372
Epoch 1/2... Batch 270... Discriminator Loss: 1.2530... Generator Loss: 0.7806
Epoch 1/2... Batch 280... Discriminator Loss: 1.1526... Generator Loss: 0.7669
Epoch 1/2... Batch 290... Discriminator Loss: 1.0764... Generator Loss: 1.1582
Epoch 1/2... Batch 300... Discriminator Loss: 1.3665... Generator Loss: 0.6395
Epoch 1/2... Batch 310... Discriminator Loss: 0.9498... Generator Loss: 1.9121
Epoch 1/2... Batch 320... Discriminator Loss: 1.3647... Generator Loss: 2.3206
Epoch 1/2... Batch 330... Discriminator Loss: 1.1483... Generator Loss: 0.9535
Epoch 1/2... Batch 340... Discriminator Loss: 1.3164... Generator Loss: 0.6301
Epoch 1/2... Batch 350... Discriminator Loss: 1.1646... Generator Loss: 1.5448
Epoch 1/2... Batch 360... Discriminator Loss: 1.1093... Generator Loss: 1.2833
Epoch 1/2... Batch 370... Discriminator Loss: 1.0916... Generator Loss: 1.1265
Epoch 1/2... Batch 380... Discriminator Loss: 1.3502... Generator Loss: 0.6226
Epoch 1/2... Batch 390... Discriminator Loss: 1.0558... Generator Loss: 1.2142
Epoch 1/2... Batch 400... Discriminator Loss: 1.6757... Generator Loss: 0.4305
Epoch 1/2... Batch 410... Discriminator Loss: 1.1281... Generator Loss: 1.8119
Epoch 1/2... Batch 420... Discriminator Loss: 0.8756... Generator Loss: 1.3510
Epoch 1/2... Batch 430... Discriminator Loss: 1.1591... Generator Loss: 0.9161
Epoch 1/2... Batch 440... Discriminator Loss: 1.0805... Generator Loss: 0.9709
Epoch 1/2... Batch 450... Discriminator Loss: 1.0274... Generator Loss: 1.0092
Epoch 1/2... Batch 460... Discriminator Loss: 0.8613... Generator Loss: 1.2742
Epoch 1/2... Batch 470... Discriminator Loss: 1.1270... Generator Loss: 0.8543
Epoch 1/2... Batch 480... Discriminator Loss: 0.9356... Generator Loss: 1.1351
Epoch 1/2... Batch 490... Discriminator Loss: 1.4562... Generator Loss: 0.4902
Epoch 1/2... Batch 500... Discriminator Loss: 1.3921... Generator Loss: 0.7610
Epoch 1/2... Batch 510... Discriminator Loss: 1.0262... Generator Loss: 1.2502
Epoch 1/2... Batch 520... Discriminator Loss: 0.8810... Generator Loss: 2.3617
Epoch 1/2... Batch 530... Discriminator Loss: 2.1886... Generator Loss: 0.2092
Epoch 1/2... Batch 540... Discriminator Loss: 1.3013... Generator Loss: 0.5930
Epoch 1/2... Batch 550... Discriminator Loss: 1.0152... Generator Loss: 1.3664
Epoch 1/2... Batch 560... Discriminator Loss: 1.0696... Generator Loss: 0.8915
Epoch 1/2... Batch 570... Discriminator Loss: 1.0708... Generator Loss: 0.8154
Epoch 1/2... Batch 580... Discriminator Loss: 1.5163... Generator Loss: 0.9363
Epoch 1/2... Batch 590... Discriminator Loss: 1.3188... Generator Loss: 0.6341
Epoch 1/2... Batch 600... Discriminator Loss: 0.9722... Generator Loss: 1.0773
Epoch 1/2... Batch 610... Discriminator Loss: 1.2738... Generator Loss: 0.6715
Epoch 1/2... Batch 620... Discriminator Loss: 1.0486... Generator Loss: 0.7579
Epoch 1/2... Batch 630... Discriminator Loss: 1.1165... Generator Loss: 1.0740
Epoch 1/2... Batch 640... Discriminator Loss: 1.1320... Generator Loss: 1.3997
Epoch 1/2... Batch 650... Discriminator Loss: 0.7753... Generator Loss: 1.5514
Epoch 1/2... Batch 660... Discriminator Loss: 1.1094... Generator Loss: 1.2128
Epoch 1/2... Batch 670... Discriminator Loss: 2.2785... Generator Loss: 0.1664
Epoch 1/2... Batch 680... Discriminator Loss: 0.8149... Generator Loss: 2.4934
Epoch 1/2... Batch 690... Discriminator Loss: 1.2708... Generator Loss: 0.6792
Epoch 1/2... Batch 700... Discriminator Loss: 0.8155... Generator Loss: 1.2877
Epoch 1/2... Batch 710... Discriminator Loss: 1.5876... Generator Loss: 0.4063
Epoch 1/2... Batch 720... Discriminator Loss: 1.6596... Generator Loss: 0.3886
Epoch 1/2... Batch 730... Discriminator Loss: 0.9737... Generator Loss: 1.2622
Epoch 1/2... Batch 740... Discriminator Loss: 0.8039... Generator Loss: 1.9559
Epoch 1/2... Batch 750... Discriminator Loss: 1.4127... Generator Loss: 0.5746
Epoch 1/2... Batch 760... Discriminator Loss: 0.8122... Generator Loss: 1.6370
Epoch 1/2... Batch 770... Discriminator Loss: 1.4099... Generator Loss: 0.5162
Epoch 1/2... Batch 780... Discriminator Loss: 1.2803... Generator Loss: 1.4328
Epoch 1/2... Batch 790... Discriminator Loss: 1.0214... Generator Loss: 0.9130
Epoch 1/2... Batch 800... Discriminator Loss: 0.5901... Generator Loss: 1.9396
Epoch 1/2... Batch 810... Discriminator Loss: 0.7616... Generator Loss: 1.4315
Epoch 1/2... Batch 820... Discriminator Loss: 1.0923... Generator Loss: 0.8112
Epoch 1/2... Batch 830... Discriminator Loss: 0.9303... Generator Loss: 1.0146
Epoch 1/2... Batch 840... Discriminator Loss: 0.8839... Generator Loss: 1.4591
Epoch 1/2... Batch 850... Discriminator Loss: 1.2350... Generator Loss: 1.3237
Epoch 1/2... Batch 860... Discriminator Loss: 1.2402... Generator Loss: 0.7776
Epoch 1/2... Batch 870... Discriminator Loss: 1.0808... Generator Loss: 1.3136
Epoch 1/2... Batch 880... Discriminator Loss: 1.2792... Generator Loss: 0.6272
Epoch 1/2... Batch 890... Discriminator Loss: 1.1193... Generator Loss: 1.9640
Epoch 1/2... Batch 900... Discriminator Loss: 0.8786... Generator Loss: 1.1791
Epoch 1/2... Batch 910... Discriminator Loss: 0.8273... Generator Loss: 2.0358
Epoch 1/2... Batch 920... Discriminator Loss: 1.0673... Generator Loss: 1.1364
Epoch 1/2... Batch 930... Discriminator Loss: 1.1320... Generator Loss: 1.3687
Epoch 1/2... Batch 940... Discriminator Loss: 1.0170... Generator Loss: 0.8247
Epoch 1/2... Batch 950... Discriminator Loss: 0.5026... Generator Loss: 2.1163
Epoch 1/2... Batch 960... Discriminator Loss: 1.4220... Generator Loss: 1.1556
Epoch 1/2... Batch 970... Discriminator Loss: 1.2608... Generator Loss: 2.1829
Epoch 1/2... Batch 980... Discriminator Loss: 1.2384... Generator Loss: 0.7244
Epoch 1/2... Batch 990... Discriminator Loss: 0.6058... Generator Loss: 2.0643
Epoch 1/2... Batch 1000... Discriminator Loss: 1.3357... Generator Loss: 0.7726
Epoch 1/2... Batch 1010... Discriminator Loss: 2.0360... Generator Loss: 0.2346
Epoch 1/2... Batch 1020... Discriminator Loss: 0.7561... Generator Loss: 1.5007
Epoch 1/2... Batch 1030... Discriminator Loss: 0.9103... Generator Loss: 1.0817
Epoch 1/2... Batch 1040... Discriminator Loss: 0.9232... Generator Loss: 1.0967
Epoch 1/2... Batch 1050... Discriminator Loss: 0.9488... Generator Loss: 1.0046
Epoch 1/2... Batch 1060... Discriminator Loss: 0.8274... Generator Loss: 3.3582
Epoch 1/2... Batch 1070... Discriminator Loss: 1.5447... Generator Loss: 0.4612
Epoch 1/2... Batch 1080... Discriminator Loss: 1.5345... Generator Loss: 0.5211
Epoch 1/2... Batch 1090... Discriminator Loss: 1.2430... Generator Loss: 1.3158
Epoch 1/2... Batch 1100... Discriminator Loss: 1.0618... Generator Loss: 1.0446
Epoch 1/2... Batch 1110... Discriminator Loss: 0.5851... Generator Loss: 1.6699
Epoch 1/2... Batch 1120... Discriminator Loss: 1.5779... Generator Loss: 0.4627
Epoch 1/2... Batch 1130... Discriminator Loss: 1.1631... Generator Loss: 0.9085
Epoch 1/2... Batch 1140... Discriminator Loss: 1.2379... Generator Loss: 0.8232
Epoch 1/2... Batch 1150... Discriminator Loss: 0.7959... Generator Loss: 1.2341
Epoch 1/2... Batch 1160... Discriminator Loss: 1.2550... Generator Loss: 0.6084
Epoch 1/2... Batch 1170... Discriminator Loss: 1.4201... Generator Loss: 0.6469
Epoch 1/2... Batch 1180... Discriminator Loss: 0.7596... Generator Loss: 1.6059
Epoch 1/2... Batch 1190... Discriminator Loss: 0.5849... Generator Loss: 2.4906
Epoch 1/2... Batch 1200... Discriminator Loss: 0.8151... Generator Loss: 3.7536
Epoch 1/2... Batch 1210... Discriminator Loss: 0.8964... Generator Loss: 2.4343
Epoch 1/2... Batch 1220... Discriminator Loss: 1.5501... Generator Loss: 0.4315
Epoch 1/2... Batch 1230... Discriminator Loss: 1.1772... Generator Loss: 1.0402
Epoch 1/2... Batch 1240... Discriminator Loss: 1.1937... Generator Loss: 0.8072
Epoch 1/2... Batch 1250... Discriminator Loss: 1.5491... Generator Loss: 0.4529
Epoch 1/2... Batch 1260... Discriminator Loss: 0.9293... Generator Loss: 1.3560
Epoch 1/2... Batch 1270... Discriminator Loss: 0.8764... Generator Loss: 1.1375
Epoch 1/2... Batch 1280... Discriminator Loss: 1.1103... Generator Loss: 0.8155
Epoch 1/2... Batch 1290... Discriminator Loss: 0.7906... Generator Loss: 3.0814
Epoch 1/2... Batch 1300... Discriminator Loss: 1.5289... Generator Loss: 0.4223
Epoch 1/2... Batch 1310... Discriminator Loss: 0.9668... Generator Loss: 1.4217
Epoch 1/2... Batch 1320... Discriminator Loss: 0.7909... Generator Loss: 1.5896
Epoch 1/2... Batch 1330... Discriminator Loss: 0.8850... Generator Loss: 1.1675
Epoch 1/2... Batch 1340... Discriminator Loss: 1.0283... Generator Loss: 0.7786
Epoch 1/2... Batch 1350... Discriminator Loss: 0.9494... Generator Loss: 0.9044
Epoch 1/2... Batch 1360... Discriminator Loss: 0.4567... Generator Loss: 3.5699
Epoch 1/2... Batch 1370... Discriminator Loss: 1.9359... Generator Loss: 0.3024
Epoch 1/2... Batch 1380... Discriminator Loss: 0.9626... Generator Loss: 1.1248
Epoch 1/2... Batch 1390... Discriminator Loss: 1.2563... Generator Loss: 0.6711
Epoch 1/2... Batch 1400... Discriminator Loss: 1.2922... Generator Loss: 0.5905
Epoch 1/2... Batch 1410... Discriminator Loss: 1.0426... Generator Loss: 1.0131
Epoch 1/2... Batch 1420... Discriminator Loss: 1.3086... Generator Loss: 1.1749
Epoch 1/2... Batch 1430... Discriminator Loss: 1.3966... Generator Loss: 0.5146
Epoch 1/2... Batch 1440... Discriminator Loss: 1.2898... Generator Loss: 0.6274
Epoch 1/2... Batch 1450... Discriminator Loss: 1.1973... Generator Loss: 0.6246
Epoch 1/2... Batch 1460... Discriminator Loss: 0.7363... Generator Loss: 1.6188
Epoch 1/2... Batch 1470... Discriminator Loss: 0.8282... Generator Loss: 1.2607
Epoch 1/2... Batch 1480... Discriminator Loss: 1.4245... Generator Loss: 0.5647
Epoch 1/2... Batch 1490... Discriminator Loss: 1.2255... Generator Loss: 0.7140
Epoch 1/2... Batch 1500... Discriminator Loss: 0.5932... Generator Loss: 2.2081
Epoch 1/2... Batch 1510... Discriminator Loss: 1.2553... Generator Loss: 0.6022
Epoch 1/2... Batch 1520... Discriminator Loss: 2.2030... Generator Loss: 3.5878
Epoch 1/2... Batch 1530... Discriminator Loss: 1.2039... Generator Loss: 0.8332
Epoch 1/2... Batch 1540... Discriminator Loss: 1.3107... Generator Loss: 0.9325
Epoch 1/2... Batch 1550... Discriminator Loss: 1.0159... Generator Loss: 1.4710
Epoch 1/2... Batch 1560... Discriminator Loss: 0.7869... Generator Loss: 1.2863
Epoch 1/2... Batch 1570... Discriminator Loss: 1.1040... Generator Loss: 1.5297
Epoch 1/2... Batch 1580... Discriminator Loss: 1.6158... Generator Loss: 0.3717
Epoch 1/2... Batch 1590... Discriminator Loss: 1.1390... Generator Loss: 0.6747
Epoch 1/2... Batch 1600... Discriminator Loss: 1.0417... Generator Loss: 0.9603
Epoch 1/2... Batch 1610... Discriminator Loss: 1.2863... Generator Loss: 0.5828
Epoch 1/2... Batch 1620... Discriminator Loss: 1.6005... Generator Loss: 0.3718
Epoch 1/2... Batch 1630... Discriminator Loss: 1.3408... Generator Loss: 0.5927
Epoch 1/2... Batch 1640... Discriminator Loss: 0.9467... Generator Loss: 1.0115
Epoch 1/2... Batch 1650... Discriminator Loss: 1.3985... Generator Loss: 0.5271
Epoch 1/2... Batch 1660... Discriminator Loss: 0.8453... Generator Loss: 1.1723
Epoch 1/2... Batch 1670... Discriminator Loss: 1.5659... Generator Loss: 0.4180
Epoch 1/2... Batch 1680... Discriminator Loss: 1.0347... Generator Loss: 1.6932
Epoch 1/2... Batch 1690... Discriminator Loss: 1.3636... Generator Loss: 2.4669
Epoch 1/2... Batch 1700... Discriminator Loss: 1.0315... Generator Loss: 0.9229
Epoch 1/2... Batch 1710... Discriminator Loss: 1.0749... Generator Loss: 0.8029
Epoch 1/2... Batch 1720... Discriminator Loss: 0.9441... Generator Loss: 3.0691
Epoch 1/2... Batch 1730... Discriminator Loss: 0.6680... Generator Loss: 1.4039
Epoch 1/2... Batch 1740... Discriminator Loss: 1.3838... Generator Loss: 0.5701
Epoch 1/2... Batch 1750... Discriminator Loss: 1.3510... Generator Loss: 0.5565
Epoch 1/2... Batch 1760... Discriminator Loss: 0.9350... Generator Loss: 0.9500
Epoch 1/2... Batch 1770... Discriminator Loss: 1.1006... Generator Loss: 0.7780
Epoch 1/2... Batch 1780... Discriminator Loss: 1.2877... Generator Loss: 0.6514
Epoch 1/2... Batch 1790... Discriminator Loss: 0.8974... Generator Loss: 1.2118
Epoch 1/2... Batch 1800... Discriminator Loss: 0.6862... Generator Loss: 2.3883
Epoch 1/2... Batch 1810... Discriminator Loss: 0.8215... Generator Loss: 1.7786
Epoch 1/2... Batch 1820... Discriminator Loss: 1.2853... Generator Loss: 1.5465
Epoch 1/2... Batch 1830... Discriminator Loss: 0.7069... Generator Loss: 1.8536
Epoch 1/2... Batch 1840... Discriminator Loss: 0.9359... Generator Loss: 1.2518
Epoch 1/2... Batch 1850... Discriminator Loss: 1.5767... Generator Loss: 0.4207
Epoch 1/2... Batch 1860... Discriminator Loss: 0.9170... Generator Loss: 1.0611
Epoch 1/2... Batch 1870... Discriminator Loss: 1.1255... Generator Loss: 0.7660
Epoch 1/2... Batch 1880... Discriminator Loss: 1.2159... Generator Loss: 0.6918
Epoch 1/2... Batch 1890... Discriminator Loss: 2.1389... Generator Loss: 0.2323
Epoch 1/2... Batch 1900... Discriminator Loss: 1.3735... Generator Loss: 1.1151
Epoch 1/2... Batch 1910... Discriminator Loss: 1.2918... Generator Loss: 0.5881
Epoch 1/2... Batch 1920... Discriminator Loss: 2.0400... Generator Loss: 0.2500
Epoch 1/2... Batch 1930... Discriminator Loss: 0.8774... Generator Loss: 1.3554
Epoch 1/2... Batch 1940... Discriminator Loss: 1.2417... Generator Loss: 0.7328
Epoch 1/2... Batch 1950... Discriminator Loss: 1.0113... Generator Loss: 1.0096
Epoch 1/2... Batch 1960... Discriminator Loss: 1.1587... Generator Loss: 1.8644
Epoch 1/2... Batch 1970... Discriminator Loss: 0.5998... Generator Loss: 2.3154
Epoch 1/2... Batch 1980... Discriminator Loss: 1.3166... Generator Loss: 1.1068
Epoch 1/2... Batch 1990... Discriminator Loss: 1.0516... Generator Loss: 1.0141
Epoch 1/2... Batch 2000... Discriminator Loss: 0.9455... Generator Loss: 1.2074
Epoch 1/2... Batch 2010... Discriminator Loss: 1.0818... Generator Loss: 0.8785
Epoch 1/2... Batch 2020... Discriminator Loss: 1.1591... Generator Loss: 1.1001
Epoch 1/2... Batch 2030... Discriminator Loss: 0.7363... Generator Loss: 1.5721
Epoch 1/2... Batch 2040... Discriminator Loss: 0.9268... Generator Loss: 1.1012
Epoch 1/2... Batch 2050... Discriminator Loss: 0.6742... Generator Loss: 1.5359
Epoch 1/2... Batch 2060... Discriminator Loss: 1.0428... Generator Loss: 0.7768
Epoch 1/2... Batch 2070... Discriminator Loss: 1.0367... Generator Loss: 0.8186
Epoch 1/2... Batch 2080... Discriminator Loss: 1.5167... Generator Loss: 0.4775
Epoch 1/2... Batch 2090... Discriminator Loss: 2.2398... Generator Loss: 3.0342
Epoch 1/2... Batch 2100... Discriminator Loss: 1.2608... Generator Loss: 0.6092
Epoch 1/2... Batch 2110... Discriminator Loss: 1.4349... Generator Loss: 0.5146
Epoch 1/2... Batch 2120... Discriminator Loss: 1.1400... Generator Loss: 1.0105
Epoch 1/2... Batch 2130... Discriminator Loss: 0.8367... Generator Loss: 1.2272
Epoch 1/2... Batch 2140... Discriminator Loss: 0.6084... Generator Loss: 2.5576
Epoch 1/2... Batch 2150... Discriminator Loss: 1.3823... Generator Loss: 0.5114
Epoch 1/2... Batch 2160... Discriminator Loss: 1.3787... Generator Loss: 0.5117
Epoch 1/2... Batch 2170... Discriminator Loss: 1.1436... Generator Loss: 0.9612
Epoch 1/2... Batch 2180... Discriminator Loss: 1.4084... Generator Loss: 0.5351
Epoch 1/2... Batch 2190... Discriminator Loss: 1.1087... Generator Loss: 0.8577
Epoch 1/2... Batch 2200... Discriminator Loss: 1.3370... Generator Loss: 0.5814
Epoch 1/2... Batch 2210... Discriminator Loss: 0.8316... Generator Loss: 1.2249
Epoch 1/2... Batch 2220... Discriminator Loss: 0.7432... Generator Loss: 1.3825
Epoch 1/2... Batch 2230... Discriminator Loss: 1.8474... Generator Loss: 1.9288
Epoch 1/2... Batch 2240... Discriminator Loss: 1.2633... Generator Loss: 0.6942
Epoch 1/2... Batch 2250... Discriminator Loss: 0.8276... Generator Loss: 1.7605
Epoch 1/2... Batch 2260... Discriminator Loss: 0.9086... Generator Loss: 1.4605
Epoch 2/2... Batch 10... Discriminator Loss: 1.0763... Generator Loss: 1.7942
Epoch 2/2... Batch 20... Discriminator Loss: 1.6339... Generator Loss: 0.3790
Epoch 2/2... Batch 30... Discriminator Loss: 1.7052... Generator Loss: 0.3619
Epoch 2/2... Batch 40... Discriminator Loss: 1.1491... Generator Loss: 0.6985
Epoch 2/2... Batch 50... Discriminator Loss: 1.1753... Generator Loss: 0.9793
Epoch 2/2... Batch 60... Discriminator Loss: 0.6698... Generator Loss: 1.9099
Epoch 2/2... Batch 70... Discriminator Loss: 2.2472... Generator Loss: 2.5552
Epoch 2/2... Batch 80... Discriminator Loss: 1.1326... Generator Loss: 0.9375
Epoch 2/2... Batch 90... Discriminator Loss: 1.2100... Generator Loss: 0.9011
Epoch 2/2... Batch 100... Discriminator Loss: 0.9165... Generator Loss: 1.2274
Epoch 2/2... Batch 110... Discriminator Loss: 1.1655... Generator Loss: 0.9062
Epoch 2/2... Batch 120... Discriminator Loss: 1.4408... Generator Loss: 0.4291
Epoch 2/2... Batch 130... Discriminator Loss: 1.6240... Generator Loss: 0.3999
Epoch 2/2... Batch 140... Discriminator Loss: 1.2155... Generator Loss: 0.6993
Epoch 2/2... Batch 150... Discriminator Loss: 1.2964... Generator Loss: 0.5478
Epoch 2/2... Batch 160... Discriminator Loss: 0.9299... Generator Loss: 1.3155
Epoch 2/2... Batch 170... Discriminator Loss: 1.3417... Generator Loss: 0.8787
Epoch 2/2... Batch 180... Discriminator Loss: 1.5867... Generator Loss: 0.3832
Epoch 2/2... Batch 190... Discriminator Loss: 0.5666... Generator Loss: 1.9618
Epoch 2/2... Batch 200... Discriminator Loss: 2.2653... Generator Loss: 3.4835
Epoch 2/2... Batch 210... Discriminator Loss: 1.2777... Generator Loss: 0.6454
Epoch 2/2... Batch 220... Discriminator Loss: 1.1505... Generator Loss: 0.7781
Epoch 2/2... Batch 230... Discriminator Loss: 1.0131... Generator Loss: 1.1188
Epoch 2/2... Batch 240... Discriminator Loss: 1.5127... Generator Loss: 0.4806
Epoch 2/2... Batch 250... Discriminator Loss: 1.1075... Generator Loss: 0.7665
Epoch 2/2... Batch 260... Discriminator Loss: 0.7810... Generator Loss: 1.1705
Epoch 2/2... Batch 270... Discriminator Loss: 1.2484... Generator Loss: 0.6964
Epoch 2/2... Batch 280... Discriminator Loss: 1.0279... Generator Loss: 0.9007
Epoch 2/2... Batch 290... Discriminator Loss: 0.9964... Generator Loss: 0.8375
Epoch 2/2... Batch 300... Discriminator Loss: 0.7634... Generator Loss: 1.4711
Epoch 2/2... Batch 310... Discriminator Loss: 0.8697... Generator Loss: 1.3348
Epoch 2/2... Batch 320... Discriminator Loss: 1.3644... Generator Loss: 0.5276
Epoch 2/2... Batch 330... Discriminator Loss: 0.8925... Generator Loss: 1.4896
Epoch 2/2... Batch 340... Discriminator Loss: 1.0983... Generator Loss: 0.9312
Epoch 2/2... Batch 350... Discriminator Loss: 1.1290... Generator Loss: 0.8585
Epoch 2/2... Batch 360... Discriminator Loss: 1.7691... Generator Loss: 0.3253
Epoch 2/2... Batch 370... Discriminator Loss: 1.2340... Generator Loss: 0.6502
Epoch 2/2... Batch 380... Discriminator Loss: 0.7522... Generator Loss: 1.7586
Epoch 2/2... Batch 390... Discriminator Loss: 1.4939... Generator Loss: 0.6440
Epoch 2/2... Batch 400... Discriminator Loss: 1.2751... Generator Loss: 0.5733
Epoch 2/2... Batch 410... Discriminator Loss: 1.3074... Generator Loss: 0.6667
Epoch 2/2... Batch 420... Discriminator Loss: 1.2761... Generator Loss: 0.6878
Epoch 2/2... Batch 430... Discriminator Loss: 1.9926... Generator Loss: 0.2551
Epoch 2/2... Batch 440... Discriminator Loss: 1.9596... Generator Loss: 0.2438
Epoch 2/2... Batch 450... Discriminator Loss: 1.0409... Generator Loss: 0.8861
Epoch 2/2... Batch 460... Discriminator Loss: 1.3203... Generator Loss: 0.6305
Epoch 2/2... Batch 470... Discriminator Loss: 1.4570... Generator Loss: 0.8651
Epoch 2/2... Batch 480... Discriminator Loss: 1.0027... Generator Loss: 1.0704
Epoch 2/2... Batch 490... Discriminator Loss: 1.8519... Generator Loss: 0.2862
Epoch 2/2... Batch 500... Discriminator Loss: 1.2358... Generator Loss: 0.7698
Epoch 2/2... Batch 510... Discriminator Loss: 1.3788... Generator Loss: 0.5282
Epoch 2/2... Batch 520... Discriminator Loss: 1.3465... Generator Loss: 0.5810
Epoch 2/2... Batch 530... Discriminator Loss: 1.1218... Generator Loss: 0.8974
Epoch 2/2... Batch 540... Discriminator Loss: 1.9517... Generator Loss: 0.2606
Epoch 2/2... Batch 550... Discriminator Loss: 1.6779... Generator Loss: 0.3502
Epoch 2/2... Batch 560... Discriminator Loss: 1.3444... Generator Loss: 0.7043
Epoch 2/2... Batch 570... Discriminator Loss: 1.0025... Generator Loss: 1.1973
Epoch 2/2... Batch 580... Discriminator Loss: 1.1865... Generator Loss: 0.6928
Epoch 2/2... Batch 590... Discriminator Loss: 1.8509... Generator Loss: 0.2962
Epoch 2/2... Batch 600... Discriminator Loss: 0.9831... Generator Loss: 1.1195
Epoch 2/2... Batch 610... Discriminator Loss: 1.1090... Generator Loss: 1.3251
Epoch 2/2... Batch 620... Discriminator Loss: 1.1543... Generator Loss: 0.8216
Epoch 2/2... Batch 630... Discriminator Loss: 1.1431... Generator Loss: 0.8198
Epoch 2/2... Batch 640... Discriminator Loss: 1.2349... Generator Loss: 0.7209
Epoch 2/2... Batch 650... Discriminator Loss: 1.1031... Generator Loss: 0.7887
Epoch 2/2... Batch 660... Discriminator Loss: 1.0080... Generator Loss: 1.0650
Epoch 2/2... Batch 670... Discriminator Loss: 1.2476... Generator Loss: 0.6254
Epoch 2/2... Batch 680... Discriminator Loss: 0.6947... Generator Loss: 2.3110
Epoch 2/2... Batch 690... Discriminator Loss: 1.1884... Generator Loss: 0.6613
Epoch 2/2... Batch 700... Discriminator Loss: 0.9422... Generator Loss: 1.7135
Epoch 2/2... Batch 710... Discriminator Loss: 1.5014... Generator Loss: 0.4982
Epoch 2/2... Batch 720... Discriminator Loss: 1.3955... Generator Loss: 0.5074
Epoch 2/2... Batch 730... Discriminator Loss: 1.0339... Generator Loss: 0.8869
Epoch 2/2... Batch 740... Discriminator Loss: 1.2506... Generator Loss: 0.6372
Epoch 2/2... Batch 750... Discriminator Loss: 1.3632... Generator Loss: 0.6649
Epoch 2/2... Batch 760... Discriminator Loss: 0.8922... Generator Loss: 1.4449
Epoch 2/2... Batch 770... Discriminator Loss: 1.4743... Generator Loss: 0.4404
Epoch 2/2... Batch 780... Discriminator Loss: 1.3266... Generator Loss: 0.6515
Epoch 2/2... Batch 790... Discriminator Loss: 1.0912... Generator Loss: 0.8806
Epoch 2/2... Batch 800... Discriminator Loss: 1.5356... Generator Loss: 0.4254
Epoch 2/2... Batch 810... Discriminator Loss: 0.9632... Generator Loss: 1.0884
Epoch 2/2... Batch 820... Discriminator Loss: 1.2077... Generator Loss: 0.6924
Epoch 2/2... Batch 830... Discriminator Loss: 1.4129... Generator Loss: 0.4994
Epoch 2/2... Batch 840... Discriminator Loss: 0.6466... Generator Loss: 1.7176
Epoch 2/2... Batch 850... Discriminator Loss: 1.3155... Generator Loss: 0.5596
Epoch 2/2... Batch 860... Discriminator Loss: 1.0286... Generator Loss: 1.3150
Epoch 2/2... Batch 870... Discriminator Loss: 0.9196... Generator Loss: 1.1404
Epoch 2/2... Batch 880... Discriminator Loss: 1.5529... Generator Loss: 0.4287
Epoch 2/2... Batch 890... Discriminator Loss: 1.2406... Generator Loss: 0.6533
Epoch 2/2... Batch 900... Discriminator Loss: 1.2800... Generator Loss: 0.5758
Epoch 2/2... Batch 910... Discriminator Loss: 1.4362... Generator Loss: 0.4957
Epoch 2/2... Batch 920... Discriminator Loss: 0.9656... Generator Loss: 0.8951
Epoch 2/2... Batch 930... Discriminator Loss: 0.7482... Generator Loss: 1.8542
Epoch 2/2... Batch 940... Discriminator Loss: 0.9588... Generator Loss: 1.3438
Epoch 2/2... Batch 950... Discriminator Loss: 1.0966... Generator Loss: 0.9840
Epoch 2/2... Batch 960... Discriminator Loss: 1.8262... Generator Loss: 0.2846
Epoch 2/2... Batch 970... Discriminator Loss: 1.0436... Generator Loss: 1.0930
Epoch 2/2... Batch 980... Discriminator Loss: 0.8999... Generator Loss: 1.5488
Epoch 2/2... Batch 990... Discriminator Loss: 0.9923... Generator Loss: 0.9718
Epoch 2/2... Batch 1000... Discriminator Loss: 1.5718... Generator Loss: 0.3927
Epoch 2/2... Batch 1010... Discriminator Loss: 1.3609... Generator Loss: 0.5599
Epoch 2/2... Batch 1020... Discriminator Loss: 1.5546... Generator Loss: 0.4126
Epoch 2/2... Batch 1030... Discriminator Loss: 0.7828... Generator Loss: 1.3247
Epoch 2/2... Batch 1040... Discriminator Loss: 1.0260... Generator Loss: 2.8303
Epoch 2/2... Batch 1050... Discriminator Loss: 1.1363... Generator Loss: 0.7740
Epoch 2/2... Batch 1060... Discriminator Loss: 1.3981... Generator Loss: 0.6682
Epoch 2/2... Batch 1070... Discriminator Loss: 1.5550... Generator Loss: 0.4102
Epoch 2/2... Batch 1080... Discriminator Loss: 1.0469... Generator Loss: 0.9379
Epoch 2/2... Batch 1090... Discriminator Loss: 1.7027... Generator Loss: 0.3498
Epoch 2/2... Batch 1100... Discriminator Loss: 0.8914... Generator Loss: 1.0224
Epoch 2/2... Batch 1110... Discriminator Loss: 0.9042... Generator Loss: 1.0752
Epoch 2/2... Batch 1120... Discriminator Loss: 1.2662... Generator Loss: 0.6541
Epoch 2/2... Batch 1130... Discriminator Loss: 0.7637... Generator Loss: 1.6108
Epoch 2/2... Batch 1140... Discriminator Loss: 0.9125... Generator Loss: 1.0771
Epoch 2/2... Batch 1150... Discriminator Loss: 1.1003... Generator Loss: 0.8089
Epoch 2/2... Batch 1160... Discriminator Loss: 1.3038... Generator Loss: 0.6068
Epoch 2/2... Batch 1170... Discriminator Loss: 1.7582... Generator Loss: 0.3172
Epoch 2/2... Batch 1180... Discriminator Loss: 1.1981... Generator Loss: 0.7363
Epoch 2/2... Batch 1190... Discriminator Loss: 1.2150... Generator Loss: 1.1040
Epoch 2/2... Batch 1200... Discriminator Loss: 1.5692... Generator Loss: 0.6025
Epoch 2/2... Batch 1210... Discriminator Loss: 1.1278... Generator Loss: 0.7516
Epoch 2/2... Batch 1220... Discriminator Loss: 1.5951... Generator Loss: 0.3897
Epoch 2/2... Batch 1230... Discriminator Loss: 1.8485... Generator Loss: 0.3584
Epoch 2/2... Batch 1240... Discriminator Loss: 1.0072... Generator Loss: 0.9375
Epoch 2/2... Batch 1250... Discriminator Loss: 1.2253... Generator Loss: 1.4595
Epoch 2/2... Batch 1260... Discriminator Loss: 1.0807... Generator Loss: 0.9954
Epoch 2/2... Batch 1270... Discriminator Loss: 0.9782... Generator Loss: 0.9481
Epoch 2/2... Batch 1280... Discriminator Loss: 1.4235... Generator Loss: 0.5022
Epoch 2/2... Batch 1290... Discriminator Loss: 1.2564... Generator Loss: 0.6891
Epoch 2/2... Batch 1300... Discriminator Loss: 1.4877... Generator Loss: 0.4861
Epoch 2/2... Batch 1310... Discriminator Loss: 1.5076... Generator Loss: 0.4518
Epoch 2/2... Batch 1320... Discriminator Loss: 0.9489... Generator Loss: 1.2116
Epoch 2/2... Batch 1330... Discriminator Loss: 1.3583... Generator Loss: 0.6434
Epoch 2/2... Batch 1340... Discriminator Loss: 0.9127... Generator Loss: 1.0610
Epoch 2/2... Batch 1350... Discriminator Loss: 1.9699... Generator Loss: 0.2634
Epoch 2/2... Batch 1360... Discriminator Loss: 1.5810... Generator Loss: 0.4002
Epoch 2/2... Batch 1370... Discriminator Loss: 1.4543... Generator Loss: 0.5086
Epoch 2/2... Batch 1380... Discriminator Loss: 1.3280... Generator Loss: 0.5714
Epoch 2/2... Batch 1390... Discriminator Loss: 1.4897... Generator Loss: 0.4540
Epoch 2/2... Batch 1400... Discriminator Loss: 1.2836... Generator Loss: 0.6635
Epoch 2/2... Batch 1410... Discriminator Loss: 1.4331... Generator Loss: 0.4957
Epoch 2/2... Batch 1420... Discriminator Loss: 1.3373... Generator Loss: 0.5353
Epoch 2/2... Batch 1430... Discriminator Loss: 1.4767... Generator Loss: 0.4786
Epoch 2/2... Batch 1440... Discriminator Loss: 0.7478... Generator Loss: 1.2080
Epoch 2/2... Batch 1450... Discriminator Loss: 0.8393... Generator Loss: 1.3928
Epoch 2/2... Batch 1460... Discriminator Loss: 1.1182... Generator Loss: 0.8620
Epoch 2/2... Batch 1470... Discriminator Loss: 0.7268... Generator Loss: 1.5321
Epoch 2/2... Batch 1480... Discriminator Loss: 1.3051... Generator Loss: 0.8266
Epoch 2/2... Batch 1490... Discriminator Loss: 1.7151... Generator Loss: 0.4551
Epoch 2/2... Batch 1500... Discriminator Loss: 1.3407... Generator Loss: 0.5793
Epoch 2/2... Batch 1510... Discriminator Loss: 0.9736... Generator Loss: 0.9092
Epoch 2/2... Batch 1520... Discriminator Loss: 1.1750... Generator Loss: 0.7748
Epoch 2/2... Batch 1530... Discriminator Loss: 0.8811... Generator Loss: 1.1523
Epoch 2/2... Batch 1540... Discriminator Loss: 1.6375... Generator Loss: 0.4239
Epoch 2/2... Batch 1550... Discriminator Loss: 2.0370... Generator Loss: 0.2516
Epoch 2/2... Batch 1560... Discriminator Loss: 1.0118... Generator Loss: 1.0948
Epoch 2/2... Batch 1570... Discriminator Loss: 1.0411... Generator Loss: 1.3467
Epoch 2/2... Batch 1580... Discriminator Loss: 2.5699... Generator Loss: 0.1342
Epoch 2/2... Batch 1590... Discriminator Loss: 1.5396... Generator Loss: 0.4337
Epoch 2/2... Batch 1600... Discriminator Loss: 1.0954... Generator Loss: 0.8180
Epoch 2/2... Batch 1610... Discriminator Loss: 1.2759... Generator Loss: 0.5871
Epoch 2/2... Batch 1620... Discriminator Loss: 1.4760... Generator Loss: 0.4726
Epoch 2/2... Batch 1630... Discriminator Loss: 1.2692... Generator Loss: 0.6778
Epoch 2/2... Batch 1640... Discriminator Loss: 0.6670... Generator Loss: 1.4869
Epoch 2/2... Batch 1650... Discriminator Loss: 1.0561... Generator Loss: 1.0499
Epoch 2/2... Batch 1660... Discriminator Loss: 1.2963... Generator Loss: 1.0874
Epoch 2/2... Batch 1670... Discriminator Loss: 1.2701... Generator Loss: 0.6007
Epoch 2/2... Batch 1680... Discriminator Loss: 0.9118... Generator Loss: 1.0217
Epoch 2/2... Batch 1690... Discriminator Loss: 1.0886... Generator Loss: 1.0065
Epoch 2/2... Batch 1700... Discriminator Loss: 1.2348... Generator Loss: 0.6304
Epoch 2/2... Batch 1710... Discriminator Loss: 2.2225... Generator Loss: 0.1921
Epoch 2/2... Batch 1720... Discriminator Loss: 1.4415... Generator Loss: 1.5746
Epoch 2/2... Batch 1730... Discriminator Loss: 1.4252... Generator Loss: 0.5337
Epoch 2/2... Batch 1740... Discriminator Loss: 1.5842... Generator Loss: 0.4688
Epoch 2/2... Batch 1750... Discriminator Loss: 1.0769... Generator Loss: 0.9449
Epoch 2/2... Batch 1760... Discriminator Loss: 1.5178... Generator Loss: 0.4631
Epoch 2/2... Batch 1770... Discriminator Loss: 1.1793... Generator Loss: 0.8495
Epoch 2/2... Batch 1780... Discriminator Loss: 1.4163... Generator Loss: 0.4931
Epoch 2/2... Batch 1790... Discriminator Loss: 1.1393... Generator Loss: 0.8539
Epoch 2/2... Batch 1800... Discriminator Loss: 1.2182... Generator Loss: 0.6326
Epoch 2/2... Batch 1810... Discriminator Loss: 1.2510... Generator Loss: 0.6184
Epoch 2/2... Batch 1820... Discriminator Loss: 1.5241... Generator Loss: 0.4085
Epoch 2/2... Batch 1830... Discriminator Loss: 1.1735... Generator Loss: 1.1734
Epoch 2/2... Batch 1840... Discriminator Loss: 1.4329... Generator Loss: 0.5318
Epoch 2/2... Batch 1850... Discriminator Loss: 1.4762... Generator Loss: 0.4784
Epoch 2/2... Batch 1860... Discriminator Loss: 1.4789... Generator Loss: 0.5749
Epoch 2/2... Batch 1870... Discriminator Loss: 0.8474... Generator Loss: 1.1801
Epoch 2/2... Batch 1880... Discriminator Loss: 1.4637... Generator Loss: 0.5145
Epoch 2/2... Batch 1890... Discriminator Loss: 1.8642... Generator Loss: 0.2773
Epoch 2/2... Batch 1900... Discriminator Loss: 0.9290... Generator Loss: 1.3316
Epoch 2/2... Batch 1910... Discriminator Loss: 1.2918... Generator Loss: 0.5943
Epoch 2/2... Batch 1920... Discriminator Loss: 1.4854... Generator Loss: 0.4621
Epoch 2/2... Batch 1930... Discriminator Loss: 1.1700... Generator Loss: 0.6894
Epoch 2/2... Batch 1940... Discriminator Loss: 1.6381... Generator Loss: 0.4054
Epoch 2/2... Batch 1950... Discriminator Loss: 1.2982... Generator Loss: 0.5600
Epoch 2/2... Batch 1960... Discriminator Loss: 1.2486... Generator Loss: 0.7413
Epoch 2/2... Batch 1970... Discriminator Loss: 1.2357... Generator Loss: 0.7058
Epoch 2/2... Batch 1980... Discriminator Loss: 1.1139... Generator Loss: 0.7915
Epoch 2/2... Batch 1990... Discriminator Loss: 1.1217... Generator Loss: 0.7320
Epoch 2/2... Batch 2000... Discriminator Loss: 1.2896... Generator Loss: 0.7000
Epoch 2/2... Batch 2010... Discriminator Loss: 0.9988... Generator Loss: 0.9761
Epoch 2/2... Batch 2020... Discriminator Loss: 0.7391... Generator Loss: 1.5058
Epoch 2/2... Batch 2030... Discriminator Loss: 1.0005... Generator Loss: 1.2883
Epoch 2/2... Batch 2040... Discriminator Loss: 1.2609... Generator Loss: 0.6602
Epoch 2/2... Batch 2050... Discriminator Loss: 1.1721... Generator Loss: 0.7847
Epoch 2/2... Batch 2060... Discriminator Loss: 1.3442... Generator Loss: 0.5491
Epoch 2/2... Batch 2070... Discriminator Loss: 1.0244... Generator Loss: 1.0179
Epoch 2/2... Batch 2080... Discriminator Loss: 1.3576... Generator Loss: 0.5953
Epoch 2/2... Batch 2090... Discriminator Loss: 1.2869... Generator Loss: 0.6918
Epoch 2/2... Batch 2100... Discriminator Loss: 1.9147... Generator Loss: 0.2872
Epoch 2/2... Batch 2110... Discriminator Loss: 1.2352... Generator Loss: 0.6718
Epoch 2/2... Batch 2120... Discriminator Loss: 0.8683... Generator Loss: 1.5219
Epoch 2/2... Batch 2130... Discriminator Loss: 1.2843... Generator Loss: 0.6327
Epoch 2/2... Batch 2140... Discriminator Loss: 1.1850... Generator Loss: 2.0573
Epoch 2/2... Batch 2150... Discriminator Loss: 1.4296... Generator Loss: 0.4737
Epoch 2/2... Batch 2160... Discriminator Loss: 1.1215... Generator Loss: 0.8385
Epoch 2/2... Batch 2170... Discriminator Loss: 1.3306... Generator Loss: 0.5376
Epoch 2/2... Batch 2180... Discriminator Loss: 1.4992... Generator Loss: 0.9247
Epoch 2/2... Batch 2190... Discriminator Loss: 1.2985... Generator Loss: 0.6463
Epoch 2/2... Batch 2200... Discriminator Loss: 1.2535... Generator Loss: 0.6065
Epoch 2/2... Batch 2210... Discriminator Loss: 0.9975... Generator Loss: 1.0095
Epoch 2/2... Batch 2220... Discriminator Loss: 1.5298... Generator Loss: 0.4573
Epoch 2/2... Batch 2230... Discriminator Loss: 1.3599... Generator Loss: 0.7042
Epoch 2/2... Batch 2240... Discriminator Loss: 1.5299... Generator Loss: 0.4107
Epoch 2/2... Batch 2250... Discriminator Loss: 1.2119... Generator Loss: 0.6309
Epoch 2/2... Batch 2260... Discriminator Loss: 1.4988... Generator Loss: 0.4685

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.